Computer system services are frequently implemented as collections of computer system entities operating collectively on one or more computer systems. The entities may utilize computer system resources including, but not limited to, processors, memory and storage as well as applications, processes and other such executable code running thereon. As access to computer system services increases, new resources may be made available to handle the increased resource demands and to avoid system slowdowns. Such dynamic changes in resource requirements may be problematic in customer datacenter environments with a fixed or finite collection of resources. If the customer datacenter environment is configured with adequate resources to handle the highest workload, costly resources may remain idle and unused while resource requirements are low. Conversely, if the customer datacenter environment is configured with a lesser amount of resources, the resources will be inadequate when workload demands increase. Lack of available resources during peak workload times may lead to reduced system performance, system outages and system failures and may significantly degrade the user experience.
One approach to addressing this cost versus performance tradeoff is that a customer may attempt to utilize system analysis tools and/or cost of ownership tools to analyze workloads. This analysis may be used to determine whether those workloads may be suitable candidates for migration to more dynamically scalable computing resource service provider environments. A potential disadvantage of conventional tools is that they generally do not provide metrics to enable identification of good candidate workloads and, as a result, a customer may attempt analysis and/or migration of workloads that are not optimally suited for such migration.